I’ve been living on my own for quite some time now, which means I don’t get to see my beautiful cat as often as I’d like. His name is Peach, and he means the world to me. He lives at my parents’ house, so this CAPTCHA is a small way to keep him close, even when we’re apart. In honor of him, I decided to name this CAPTCHA “Peach CAPTCHA.”
To solve the CAPTCHA, simply outline Peach, my beloved cat, in the image—don’t forget the tail! Once you’ve traced his whole body, click the “Validate CAPTCHA” button to see if you’ve passed.
Increased speed by 8x.
You will need Git and Docker already installed on your system. Your system also has to be able to execute make -v command
git clone https://github.com/DewFill/peach-captcha-php.gitThis command will build a Docker image, run all containers and install Composer
make startThe application will run on http://localhost:80.
# Stop all containers without losing data
make stop# Delete all containers, volumes, and networks
make clear# Build the image
make build-image# Open a Bash shell in the PHP container
make bashAPI endpoints are not included in this document. For a complete list of available endpoints, please refer to the registerStandardHandlers method in the HttpHandler class.
- http://localhost:80 - Generates new CAPTCHA. You have ~40 seconds to solve it.
- http://localhost:80/mask - Creates a new CAPTCHA.
- http://localhost:80/mask?mask_id={id} - Edits an existing CAPTCHA.
Algorithm for Calculating Point Match Percentage Between Masked and User Paths
This algorithm is designed to analyze the match of points between two data sets: masked paths and user paths. It computes two key metrics: the percentage of unique points from the mask affected by the user and the percentage of user points that did not match with mask points.
You can find the programmatic version of the algorithm in the calculatePathSimilarity method of the CaptchaHandler class.
The algorithm takes two parameters:
- maskPaths: An array of masked paths, each containing an array of points.
- userPaths: An array of user paths, also consisting of arrays of points.
Each point is represented by coordinates (e.g., x and y), and a predefined tolerance is used to evaluate their match.
- Initialization of Arrays:
- Two arrays are created: matchedPoints1 to store unique points from the mask that match user points, and matchedPoints2 to store user points that match with the mask.
- Comparing Mask Points with User Points:
- The algorithm iterates through each point from the masked paths and compares it with each point from the user paths.
- If a masked point matches a user point within the defined tolerance, it is added to the matchedPoints1 array. Only one match for each unique masked point is considered, leading to a transition to the next masked path.
- Comparing User Points with Mask Points:
- This part of the algorithm performs the reverse action: it iterates through each point from user paths and checks for matches with points from the mask.
- All matches are added to the matchedPoints2 array, including duplicates, allowing the algorithm to account for the number of misses.
- Calculating the Total Number of Points:
- To compute match and miss percentages, the algorithm determines the total number of points in both data sets. This is done using a helper function that sums the number of points in each path.
- Calculating Percentages:
- The percentage of matches (percentageMatched1) is calculated as the ratio of the number of unique matches to the total number of points in the mask. If there are no points in the mask, it returns 0.
- The percentage of misses (percentageNotMatched2) is determined as the difference between the total number of user points and the number of matches, divided by the total number of user points. If the user paths are empty, it returns 0.
The result of the algorithm is an array containing two values: the percentage of unique points from the mask that were affected by the user and the percentage of user points that did not match the mask. This analysis provides insights into how effectively the user followed the designated path and highlights areas of discrepancy.
The security of this application has not been tested.
Anyone can edit the CAPTCHA settings, as the admin endpoints do not require authentication.
This application was manually tested on Apple Silicon macOS computer
- Unit Tests are not available
- End-to-end Tests are not available
- Behavioral Tests are not available
Bind for 0.0.0.0:80 failed: port is already allocated
If port 80 is already in use, you can easily override it. Simply create a deployment/docker-compose.override.yml file and copy the contents from deployment/docker-compose.override.yml.example into it. After that, start the application as usual.
This CAPTCHA requires the ability to visually identify and outline objects in an image, which may not be accessible to individuals who are visually impaired, people with cognitive or motor impairments, or users relying on screen readers. Additionally, slow internet connections may affect the loading and solving process. Consider using alternative CAPTCHA methods, such as audio or text-based challenges, to ensure broader accessibility.
- Take a look at LaurieWired on YouTube. I especially enjoyed the videos "What ACTUALLY happens during a Stack Overflow?" and "How a Clever 1960s Memory Trick Changed Computing".
- Check out BroskiReport. She's fun, and I enjoy listening to her podcast in the background.
- Stream Brat by Charli XCX.
- Check out my Telegram channel. I occasionally post educational content about PHP.